Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
Embodiments relate to storage and management of data, and in particular to methods and apparatuses implementing overlay visualizations utilizing a data layer. Specific embodiments allow different information types to be displayed along a same axis of a chart.
Databases and overlying applications referencing data stored therein, offer a powerful way of storing and analyzing large volumes of data that are related in various ways. In particular, discrete values of stored data may be organized into larger data structures comprising related fields.
Such larger data structures may also be referred to as data objects, and can be represented in the form of tables having rows and columns. Through the skillful and intuitive presentation of such data structures in the form of tables and charts, a user can describe complex issues and the factual/forecast data underlying those issues.
In analytics applications, data fields are generally divided into two categories: measures and dimensions. Measures are typically continuous numerical values, while dimensions are mostly categorical and discrete.
To create charts, a user can assign one or more fields to each of the axes. For example in a column chart a user can assign a dimension to the horizontal axis, and assign a measure to the vertical axis representing values of the measure for each distinct value of the dimension.
On occasion, it may be desired to plot one measure based upon two dimensions from the same value set. One example could be to plot a number of tasks for a first dimension representing task start date, and a second dimension representing task end date.
The underlying data that is to be visualized, may be present within a data layer in Structured Query Language (SQL) tabular format. However such a data layer allowing data visualizations by mapping dimensions and measures to chart axes, may not permit a same shared axis to be used for more than one set of values.
In an attempt to overcome this problem, conventional approaches may rely upon a visualization layer to separately generate charts, and then modify those charts to superimpose them. Such an approach may add complexity and expense by consuming significant processing resources in the visualization layer. This problem may be exacerbated where data in the data layer is being updated, and the visualization layer is forced to generate new visualizations and freshly superimpose them each time.